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AIArtificial Analysis reports that Google's Gemini 4 Argon scores 53 on its Intelligence Index with high reasoning, matching GPT-6 Astra (max) and one point ahead of GPT-6.1 Sol (max). At the current 50% launch discount, its cost per task is $1.99, about 60% of GPT-6 Astra's $3.26, but the discount's end date is unconfirmed and standard pricing would raise it to $3.98. The model is being rolled out to selected users and is not publicly available.

Why it matters: The benchmark compares Gemini 4 Argon's cost per task and hallucination rate with GPT-6 Astra, showing where its value depends on a temporary 50% discount.

AILiquid AI announced d1, its first decision model, which it says is the first to outperform Jev on Hugging Face's Decision Index. The company claims d1 wins on multilingual evals, resists prompt injection better, handles longer inputs more effectively, and is built for fast, structured decision-making in software environments. It is available via the Liquid API at console.liquid.ai, with OpenRouter availability coming soon.

AIOpenAI's Sam Altman introduces 6.1 Sol, a model with near-Astra intelligence priced at one fifth of Astra and a 95% cache read discount. The quoted post says ultrafast mode gives 8X speed for Astra today and will come to 6.1 Sol soon.

AIBAAI released AREX-2, a 27B-parameter long-horizon agent model that improves solutions over multiple test-time rounds by proposing, measuring, reflecting, and revising. It was trained on machine-learning and algorithmic-programming tasks with verifiable feedback, and the source reports that this self-improvement transfers to deep research. The model is Apache License 2.0 licensed and has a 262,144-token context length.

Why it matters: The source compares AREX-2 against closed and open models on coding and deep-research benchmarks, showing how test-time self-improvement is measured across task types.

AIOpenAI says GPT-6.1 Sol is available starting today to all Plus, Pro, Business, Enterprise, and Edu users. The model is offered in ChatGPT Work and Codex, and the post links to OpenAI's introduction page.

AIOpenAI reports that GPT-6.1 Sol shows major alignment improvements over GPT-6 Sol in its evaluations, moving closer to GPT-6 Astra. The model is more transparent about its limitations and more reliable at respecting user intent and safety constraints.

AIModelScope announced IQuest-Q1, a 320B MoE model with 15B active parameters and a 512K context window for agentic coding. The post reports scores of 84.5 on CyberGym, 83.2 on Terminal-Bench 2.1, 64.6 on DeepSWE v1.1, and 63.0 on NL2Repo, and says weights are released under the IQuest-Q1 License.

AIShanghai AI Laboratory's Intern-Decision family of 0.8B, 2B, and 4B multimodal models averages 79.38, 84.68, and 90.02 across seven decision benchmarks. Intern-Decision-4B scores 88.74, surpassing Jev while achieving better probability calibration. Reported mean latency is 33.98, 33.28, and 44.16 ms, versus 109.70 ms for Jev in the same local HF setup.

AIInternLM's AutoVerifier, built on Qwen3_5MoeForConditionalGeneration with about 68 GiB of weights across 40 safetensors shards, evaluates natural-language mathematical proofs, explains errors, and identifies the earliest incorrect step. It serves as the automatic grader for AdvancedMathBench's ProverBench, which accepts a proof only when all eight judgments report -1. The model is a learned grader rather than a formal proof checker and can make errors.

AIArtificial Analysis reports that GPT-6.1 Sol replaces GPT-6 Sol after seven days and scores 1 point below GPT-6 Astra on the Intelligence Index. At max effort it costs $0.72 per Intelligence Index task, compared with $3.26 for GPT-6 Astra and $1.05 for GPT-6 Sol. Its pricing matches GPT-6 Sol at $2/$10 per million input/output tokens, but it uses about 10-30% more output tokens.

Why it matters: The source compares GPT-6.1 Sol against GPT-6 Sol, GPT-5.6 Sol, and GPT-6 Astra on cost per task and token use, helping readers weigh performance against price.

AIAudio8 ASR Infinite transcribes Chinese and English audio of unlimited length using a rolling KV Cache that avoids accumulated drift. At a 480 ms delay, it reports 1.75 CER on AISHELL-1, 2.89 on AISHELL-4, and 3.04/6.81 WER on LibriSpeech test-clean/test-other. The preview release is under Apache 2.0, with deployment through an adapted vLLM stack.

AIAnthropic has released Claude Sonnet 5.5, the second model in the Claude 5.5 family. The company says it is more than 30% faster than Sonnet 5 and costs up to 30% less for most work.

Why it matters: The post gives concrete speed and price changes against Sonnet 5, which helps readers judge whether the upgrade fits their workloads and budgets.

AIAnthropic's Cat Wu says Claude Sonnet 5.5 lets Claude Code users complete about 30% more tasks than with Sonnet 5. The model needs fewer tokens for the same work, and in a leaf-raking tool-call demo it finished 24 seconds faster using 6K fewer tokens.

Why it matters: The post gives a measured Claude Code task-completion gain and a token-use example, showing what the model upgrade means for a coding agent workflow.

AIAnthropic's Claude Sonnet 5.5, the second model in the Claude 5.5 family, is shown fixing a bug in Claude Code. Boris Cherny says it runs 30% faster and uses 30% less usage, and Anthropic's announcement says it runs over 30% faster and costs up to 30% less for most work.

AIAnthropic launched Sonnet 5.5, which the post says is smarter and more tasteful than Sonnet 5. It is positioned for work that does not need the extra capability of Opus or Fable.

AIAnthropic has released Claude Sonnet 5.5, the second model in the Claude 5.5 family. The quoted announcement says it is a clear upgrade over Sonnet 5, runs more than 30% faster, and costs up to 30% less for most work.

AIJina-OCR-v1, a 3.4B-parameter MoE model that activates 570M parameters per token, converts entire document pages into structured Markdown at 2.57 pages per second. It scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, 7.4 points above DeepSeek-OCR on the latter, and delivers the highest throughput among 14 evaluated systems at concurrency 32. The model is released under CC BY-NC 4.0, so commercial use requires permission.

AIAnthropic has launched Claude Sonnet 5.5, the second model in its Claude 5.5 family. The company describes it as a faster, lower-cost complement to Claude Opus 5.5, and points readers to a blog post for more information.

Why it matters: The release note places Sonnet 5.5 beside Opus 5.5 in the Claude 5.5 family, clarifying which model suits speed and cost needs.

AIXiaomi has released MiMo-V2.6-Flash-MOPD on Hugging Face, an upgrade of the MiMo-V2.6-Flash-RL checkpoint that fuses several domain-specialized teachers into one model. The sparse MoE model has 309B total and 15B activated parameters, a 1M-token context length, and supports text, image, video, and audio inputs. The checkpoint targets tool-call repetition, a failure mode where the model repeatedly issues the same or similar tool calls without making progress.

AIXiaomi has released MiMo-V2.6-Pro-MOPD, an upgrade of the MiMo-V2.6-Pro-RL checkpoint that fuses several domain-specialized teachers into one model via MOPD2 and targets tool-call repetition. The sparse MoE model has 1.02T total and 42B activated parameters, a 1M-token context length, and accepts text, image, video, and audio inputs. Weights are available on Hugging Face and ModelScope, with deployment recipes for SGLang and vLLM.

AIShanghai AI Lab's InternLM released Intern-Decision-4B, a multimodal structured decision model fine-tuned from Qwen3.5-4B, which returns answer distributions for multiple questions in one forward pass. On its benchmark table it scores an average of 90.02 with a Brier score of 0.347 and an ECE of 0.065, and per-query latency averages 44.16 ms on a single RTX 4090. The model is available with a Python DecisionEngine inference interface.

AIShanghai AI Lab's InternLM released Intern-Decision-2B, a multimodal structured decision model fine-tuned from Qwen3.5-2B that returns calibrated answer distributions for multiple questions in one forward pass. It averages 84.68 across listed benchmarks with a 0.437 Brier score and 33.28 ms mean latency on a single RTX 4090. Model weights, a Python DecisionEngine API, and GitHub code are available, with support for up to 16 questions and eight images.

AIInternLM released Intern-Decision-0.8B, a multimodal structured decision model fine-tuned from Qwen3.5-0.8B that scores answers to multiple questions in one forward pass. The model reports a 79.38 average score and a 33.98 ms mean latency on a single RTX 4090, with 0.8B, 2B, and 4B sizes available. It is accessed through a Python DecisionEngine API that returns calibrated probabilities rather than generating free-form text.

AIMeituan's LongCat team has released LongCat-2.5-Preview, a natively multimodal model with 1.6T total parameters, about 48B active, and a 1M-token context window. The model is built for long-horizon tasks spanning terminals, browsers, GUIs, spreadsheets, and design tools. It is available now through an API on the LongCat platform and a chat interface.

AIModelScope has released NeoHorse-Jev-4B, a compact open model that converts application states into structured decisions and probabilities. It scores 77.70 across six text decision benchmark groups, ranking first among four open-weight models with complete results in the comparison. Its prefill-only inference supports Choice, Noul, and Score primitives, accepts text or a single image with text, and is available under Apache 2.0 for deployment via vLLM, SGLang, Python, CLI, or HTTP.

AIOdyssey introduced Agora-2, its next-generation multi-agent world model, which supports up to 20 humans and agents interacting in a shared environment simulated in real time. A multiplayer research preview is available to try now.

AIGoogle's Philipp Schmid announced Gemini 3.8 Flash, recommending Gemini for multimodal understanding. A quoted post by Spencer Schiff reported that frontier models struggled to match correct names to people in a drawing, offering a visual test for future models.

AIModelScope released Qwen-Image-2.1-Fun-Controlnet-Union, a single checkpoint adding eight structural controls, including Canny, Depth, Pose, and Scribble, plus inpainting to Qwen-Image 2.1. Control and inpainting share one branch with 16 injection points across every second Transformer block, keeping the base model frozen and requiring no checkpoint switching. It runs at guidance scale 1.0 with CFG-distilled sampling and prefix KV caching, and is available under the Qwen Research License with base Qwen-Image 2.1 weights required.

AIAlex Wortega releases openjev, a family of Jev-style models at 0.8B, 2B, 4B, and 35B-A3B for reranking, grading, guardrails, and real-time game decisions. The models come with SGLang serving support, according to the post.

AIBlack Forest Labs says FLUX 3 Action can control a simulated drone to carry out a specific requested action. The post offers no further details on benchmarks, availability, or implementation.

AIBlack Forest Labs says FLUX 3 Action takes recent camera frames, the system's current state, and a task description to return the next 32 actions. The model also predicts how the scene will change, and runs a continuous observe-plan-act-adjust loop. It can recover from its own mistakes, according to the company.

AIBlack Forest Labs says FLUX 3 Action builds on the same image, video, and audio pretraining as FLUX 3 but uses a smaller architecture. The company attributes this smaller size to more efficient representations learned through its Self-Flow research. During midtraining, the model was trained to predict actions and future frames together.

AIBlack Forest Labs says its FLUX 3 Action, a single-step 7B checkpoint, outperforms every other open policy on RoboLab. It processes each second of robot motion 1.45× to 1.66× faster than Pi0.5, and uses a 2.13-second action horizon versus Pi0.5's 1 second. The company adds that its guidance-distilled checkpoint raises the state-of-the-art RoboLab success rate while running 2.85× to 3.15× faster than the previous leading open WAM.

AIBlack Forest Labs says FLUX 3 Action is an open-weights 7B world action model that ranks first on the RoboLab benchmark. The company says it outperforms the previous best open model by 6.1 percentage points while using 56% fewer parameters and running up to 3.95x faster. The model predicts video and actions together, and the company is releasing the weights, code, fine-tuning recipe, benchmarks, and examples. It also integrated the model into Hugging Face's LeRobot with NVIDIA, with edge deployment on NVIDIA Jetson.

Why it matters: The release pairs benchmark results with the trade-off it claims to remove between world action model performance and VLA speed, which is useful context for robotics teams weighing open models.

AIBlack Forest Labs has published a blog post and downloadable weights for its FLUX 3 Action model, with links to a Hugging Face collection. The post also invites readers to contact the company's robotics team.

AIMicrosoft AI reports its image model rose from 864 to 1147 Elo in nine months, a gain of 377 points per year. The company says that pace is 1.6 times the next-fastest climb on the leaderboard. The post does not name the model.

AIGoogle introduces Gemini 3.8 Flash TTS for creative voice design and Gemini 3.8 Flash-Lite TTS for high-volume, cost-efficient speech generation. Flash TTS supports voice creation from natural language prompts across more than 100 languages and dialects, and both models are rolling out today in the Gemini API and Google AI Studio, with enterprise access coming soon via Gemini Enterprise.

AIGoogle has released Gemini 3.8 Flash TTS, a text-to-speech model that costs less than its previous 3.1 Flash TTS model. The new model is accompanied by a new experience in Google AI Studio.

AIGoogle introduced Gemini 3.8 Flash and Flash-Lite TTS, new state-of-the-art text-to-speech models. They add a new voice design experience, over 2,000 production-ready voices, voice replication, and support for 100 languages. The models rank first on Hume AI's voice benchmarks, and voice remixing is coming soon.

AIGoogle announced two new Gemini 3.8 text-to-speech models, positioned as its most expressive yet. Gemini 3.8 Flash TTS targets creative work, letting developers use natural language to define vocal personas, cues, pacing, and dialects, while Gemini 3.8 Flash-Lite TTS is built for high-volume pipelines such as bulk audiobook production and audio dubbing. Both are available now through the Gemini API in Google AI Studio.